首页|Gladstone Institutes Reports Findings in Machine Learning (SuPreMo: a computatio nal tool for streamlining in silico perturbation using sequence-based predictive models)
Gladstone Institutes Reports Findings in Machine Learning (SuPreMo: a computatio nal tool for streamlining in silico perturbation using sequence-based predictive models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from San Francisco, Californi a, by NewsRx journalists, research stated, "The increasing development of sequen ce-based machine learning models has raised the demand for manipulating sequence s for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, such as incompatibility with structura l variants, challenges in identifying responsible sequence perturbations, and th e need for vcf file inputs and phased data." Funders for this research include National Institutes of Health, Additional Vent ures, and Gladstone Institutes. The news correspondents obtained a quote from the research from Gladstone Instit utes, "To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing and supportin g in silico mutagenesis experiments. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize p athogenic variants or discover new functional sequences. SuPreMo was written in Python, and can be run using only one line of code to generate both sequences an d 3D genome disruption scores."
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